Fast Delay-Multiply-and-Sum Beamformer: Application to Confocal Microwave Imaging

Karam, Seyed Abbas Shah; Asl, Babak Mohammadzadeh

doi:10.1109/lawp.2019.2951575

Cited by 18 publications

(7 citation statements)

References 26 publications

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“…The sensitivity-maps method also successfully detected a more complicated object with multiple permittivity contrast where this is still difficult to achieve with CT-based methods. This study also shows better spatial resolution due to higher frequency and broader frequency, as well as complex object with multiple contrast, and faster reconstruction compared to other popular qualitative microwave imaging technique mentioned before such as microwave holographic imaging [10]- [12] and confocal imaging [13]- [15]. Table 5 shows comparison between sensitivity-maps method and previous methods as well as other popular qualitative method.…”

Section: Discussionmentioning

confidence: 66%

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The Performance of Sensitivity-Maps Method in Reconstructing Low Contrast and Multi-Contrast Objects for Microwave Imaging Applications

Basari

Ramdani

2023

Int. J. Onl. Eng.

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The microwave imaging system for breast tumor/cancer detection requires high sensitivity to detect abnormal tissue that has little contrast in high-density breasts. This paper proposes a qualitative microwave imaging system simulation based on inverse scattering using the sensitivity-maps method. This method utilizes two measurement types for system calibration: a reference object as a scatterer-free background and a calibration object to obtain the system's impulse response. The object under test (OUT) consists of an object with low dielectric contrast and a phantom with multiple low dielectric contrasts (multi-contrast). Reconstruction is carried out on three types of S-parameter measurement data, namely S_11,〖 S〗_21, and a combination of both. S-parameters are measured at several frequencies, which are 3, 10, 14, 15, 16, 20 GHz, and the combination of all those frequencies (multifrequency). Reconstructed images show that the system is capable of reconstructing dielectric objects accurately. Quantitatively, the results show that the multifrequency S_21 measurement yields the best image quality with relative root mean squared error (RRMSE) values of 0.1272 and structural similarity index (SSIM) of 0.9076. The designed imaging system also successfully reconstructs multi-contrast phantom accurately with RRMSE of 0.1434 and SSIM of 0.4609.

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Section: Discussionmentioning

confidence: 66%

“…Several qualitative reconstruction methods commonly used include microwave holographic imaging [10]- [12], confocal imaging [13]- [15], and time-reversal imaging [16]- [18]. These three methods can reconstruct the image in a relatively fast time.…”

Section: Introductionmentioning

confidence: 99%

The Performance of Sensitivity-Maps Method in Reconstructing Low Contrast and Multi-Contrast Objects for Microwave Imaging Applications

Basari

Ramdani

2023

Int. J. Onl. Eng.

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“…Future work will be done to optimize it further to obtain the exact dimension of the malignant target. The image quality and contrast can also be improved through delay and sum (DAS) [35], [36], delay multiply and sum (DMAS) [37]- [39] and channel-ranked delay and sum (CR-DAS) [40], [41] algorithms.…”

Section: Imaging Of Stroke and Brain Atrophymentioning

confidence: 99%

Design and Evaluation of Wearable Multimodal RF Sensing System for Vascular Dementia Detection

Anwar

Arslan

Hussain

et al. 2023

IEEE Trans. Biomed. Circuits Syst.

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Vascular dementia is the second most common form of dementia and a leading cause of death. Brain stroke and brain atrophy are the major degenerative pathologies associated with vascular dementia. Timely detection of these progressive pathologies is critical to avoid brain damage. Brain imaging is an important diagnostic tool and determines future treatment options available to the patient. Traditional medical technologies are expensive, require extensive supervision and are not easily accessible. This paper presents a novel concept of lowcomplexity wearable sensing system for the detection of brain stroke and brain atrophy using RF sensors. This multimodal RF sensing system provides a first-of-its-kind RF sensing solution for the detection of cerebral blood density variations and blood clots at an initial stage of neurodegeneration. A customized microwave imaging algorithm is presented for the reconstruction of images in affected areas of the brain. Designs are validated using software simulations and hardware modeling. Fabricated sensors are experimentally validated and can effectively detect blood density variation (1050 ± 50 Kg/m 3 ), artificial stroke targets with a volume of 27 mm 3 and density of 1025-1050 Kg/m 3 , and brain atrophy with a cavity of 58 mm 3 within a realistic brain phantom. The safety of the proposed wearable RF sensing system is studied through the evaluation of the Specific Absorption Rate (SAR < 1.4 W/Kg, 100mW) and thermal conductivity of the brain (< 0.152 °C). The results indicate that the device is viable as an efficient, portable, and low-cost substitute for vascular dementia detection.

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“…where M is the total number of received signals and A faster version of DMAS (FDMAS) was proposed in [20], where energy is calculated as follows:…”

Section: Principle Of Radar-based Algorithmsmentioning

confidence: 99%

“…The DMAS gains better resolution and contrast at the cost of increasing the computational complexity to O(M 2 ) [16]. The advantages of using DMAS over DAS has been proven in [20,21]. The iterative variant of DMAS was proposed in [22], where the background clutter was removed in the resulting images.…”

Section: Introductionmentioning

confidence: 99%

Parallel Delay Multiply and Sum Algorithm for Microwave Medical Imaging Using Spark Big Data Framework

Ullah

Arslan

2021

Algorithms

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Microwave imaging systems are currently being investigated for breast cancer, brain stroke and neurodegenerative disease detection due to their low cost, portable and wearable nature. At present, commonly used radar-based algorithms for microwave imaging are based on the delay and sum algorithm. These algorithms use ultra-wideband signals to reconstruct a 2D image of the targeted object or region. Delay multiply and sum is an extended version of the delay and sum algorithm. However, it is computationally expensive and time-consuming. In this paper, the delay multiply and sum algorithm is parallelised using a big data framework. The algorithm uses the Spark MapReduce programming model to improve its efficiency. The most computational part of the algorithm is pixel value calculation, where signals need to be multiplied in pairs and summed. The proposed algorithm broadcasts the input data and executes it in parallel in a distributed manner. The Spark-based parallel algorithm is compared with sequential and Python multiprocessing library implementation. The experimental results on both a standalone machine and a high-performance cluster show that Spark significantly accelerates the image reconstruction process without affecting its accuracy.

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Fast Delay-Multiply-and-Sum Beamformer: Application to Confocal Microwave Imaging

Cited by 18 publications

References 26 publications

The Performance of Sensitivity-Maps Method in Reconstructing Low Contrast and Multi-Contrast Objects for Microwave Imaging Applications

The Performance of Sensitivity-Maps Method in Reconstructing Low Contrast and Multi-Contrast Objects for Microwave Imaging Applications

Design and Evaluation of Wearable Multimodal RF Sensing System for Vascular Dementia Detection

Parallel Delay Multiply and Sum Algorithm for Microwave Medical Imaging Using Spark Big Data Framework

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